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Supply chain AI agents
Four pillars of supply chain decision making. Every agent sits in one.
Supply chain decisions cluster into four families, and they are not independent: what demand is really doing changes what the plants should run, which changes what you should hold, which changes what you can promise. A2go's agents are organized the same way — each one purpose-built for a decision inside a pillar, all of them reasoning on one shared foundation.
The four pillars
Where the decisions live.
Each pillar covers a family of decisions with its own rhythm — a forecast that moves weekly, a schedule that moves daily, a promise that moves in minutes. The agents inside a pillar share its data and its constraints, and the pillars share with each other.
Forecasting & Planning
What demand is really doing, ahead of the report that says so. Internal and external demand sensing, supplier and distributor forecasting, SKU-level forecasting, and full-horizon forecast and planning.
Moves forecast accuracy and the cost of reactive replanning
Operational Planning
What the plants can actually run this week. Master production scheduling and S&OP optimization, purchase order excellence, lead-time and safety-stock optimization, and scenario analysis across both.
Moves schedule attainment and planning cycle time
Supply & Inventory Optimization
What to hold, where to hold it, and what to stop holding. Classification, slow-moving inventory, multi-echelon inventory optimization, vendor-managed inventory opportunity, and supplier reliability.
Moves working capital and inventory health
OTIF Optimization
What you can safely promise, and what is already at risk. Capable-to-promise, promise-date jeopardy, unexpected customer orders, customer promise intelligence, and revenue and OTIF optimization.
Moves on-time in-full and revenue at risk
In concert
The pillars are not four products. They are one conversation.
A single agent answering one question well is useful. A set of agents answering across the chain — against one another, continuously — is what changes the decision. Before anything reaches a planner, the pillars have already exchanged what each of them knows and settled the tradeoffs between them.
Context passes forward, constraints pass back, and the result is one reconciled recommendation rather than four competing ones.
A shift in sensed demand does not stop at the forecast. It reaches master scheduling as a change in what the plants should be building, before the monthly cycle would have surfaced it.
A resequenced build changes what needs to be on hand and where. Safety stock, replenishment, and slow-moving positions are re-evaluated against the schedule that will actually run.
What is genuinely available — across plants and DCs, net of what the schedule has committed — is what capable-to-promise answers with. Not a static availability figure.
A promise that cannot be held without breaking a higher-priority commitment returns as a constraint on the plan. The loop closes rather than escalating to a person.
Four pillars, one recommendation, and the tradeoffs already reconciled before a planner sees it.
How the tradeoffs get settled
Optimal for the business, not optimal for one function.
Left alone, each pillar would optimize itself: inventory would carry less, service would carry more, the plant would run the longest campaigns it could. The coordination is what stops four locally correct answers from producing one bad outcome.
Shared inputs
One reconciled view.
Every agent draws from ADOE, so the demand number in the schedule is the demand number in the promise. Disagreements between agents are about tradeoffs, never about whose data is right.
Your priorities
Ranked by your rules.
When pillars conflict, the Judgment Layer decides how the conflict is weighed — which account is protected, when cost yields to service, which supplier is trusted in which season.
The output
A decision package.
What triggered it, the alternatives considered, the constraint that applied, and the expected impact — ranked, with the tradeoffs visible, ready for a person to approve, edit, or reject.
- Inside ADIPThe platform underneath the agents, layer by layer.
- The Judgment LayerHow your priorities get encoded, and how they sharpen with every approval.
- How it runsApproval, audit, and write-back into your systems of record.
- What you do not have to do firstWhy none of this requires a data migration.
- Worked decision examplesDecision packages traced end to end, from trigger to write-back.
Purpose-built, not repurposed
Every agent exists because a supply chain decision was costing someone money.
A2go's agents were not adapted from general-purpose building blocks and pointed at supply chain afterward. Each one starts from a decision manufacturers and distributors actually lose money on — a schedule that will not hold, a promise date at risk, inventory sitting in the wrong place — and each is tied to a measurable financial metric.
The decisions they are built for
Multi-plant master production scheduling
Sequencing across sites that share supply, capacity, and customers — the decision our first production deployment was built on.
Multi-level bills of material
One purchased component moving through three BOM levels into four finished goods, traced without a planner rebuilding it in a spreadsheet.
Capacity and constraint reasoning
What the line can actually run this week, against what the plan assumes it can.
Batch variability and yield
Process environments where the input is not uniform and the output cannot be assumed — including perishability and shelf-life constraints.
MES and OT signals
What the floor knows, in the same decision as what the ERP recorded — rather than in a separate report nobody reads in time.
Multiple ERPs
Reconciled against the definitions that exist today, with no requirement to standardize the estate first.
Start here
Which decision would you start with?
Bring the one your team loses the most time or margin on. We will show which agents would touch it, what they would need to reason on, and what would reach your planner.